It is often necessary or desirable to be able to locate user equipment devices (UE) within a communications network. Certain services offered via the network may be geographically dependent, and several aspects of network management may require the accurate location of UEs within the network and tracking their movements through the network. These network management tasks may include handoff control, code division, and Location Assisted Network Management, such as Location-Aided Handover for example.
Global positioning satellites may be used by some UEs to determine their geographical location. However, not all UEs are equipped with GPS capability, and even when available to a UE, GPS based location is not always reliable, as UEs can experience difficulty in receiving GPS signals owing to the surrounding environment. Urban and/or indoor environments may be particularly problematic, and many UEs are predominantly located in such environments. Difficulties may also be encountered in the sharing of GPS positioning information with a network entity and with access costs. Consequently, it would be desirable for networks to be able to locate UEs within the network independently, without reliance on external GPS technology.
The increasing availability of mobile devices, and improvements in sensing technologies, have made available a large amount of information which may be used in algorithms for locating UEs within a network without additional positioning information from satellites. This information may be used in both synchronous and asynchronous networks, although in asynchronous networks, the task is rendered more complicated by timing considerations, as discussed below.
In asynchronous networks, such as UMTS networks for example, the basestations and UEs within the network are not required to lock to a stable reference clock. Consequently, the timing signal in basestations and UEs is less accurate than in a synchronous communication system, and the phase of the timing signal may drift over time. This timing difference can greatly complicate the process of locating UEs within the network.
Location processes in communication networks generally require an initial assistance step, in which information is provided for position estimation. A series of measurements may then be carried out by a UE and reported to a location entity, which may then determine the position of the UE based on the reported measurements. The measurements may include the angle of arrival (AOA) of a signal, received signal strength (RSS) of a signal, time of arrival (TOA) of a signal and/or time difference of arrival (TDOA) of two signals. TDOA has been adopted by the 3rd Generation Partnership Project, where it is referred to as observed time difference of arrival (OTDOA), as it represents the perceived time difference between arrival of signals from two different basestations.
OTDOA techniques involve the application of a multilateration process based on measurement reports received from UEs. This process is described below with reference to FIG. 1. The process uses the propagation delay of radio signals received at a UE from several different basestations to infer the geographic distance of the UE from the basestations. In a first step, signals received from three or more separate basestations at known locations (e.g. sites 1 to 4 of FIG. 1) are measured by a single UE 5 and reported in the form of a measurement report. The propagation delay measurements of the report are manipulated to take the form of time difference measurements, as opposed to absolute time measurements. Each time difference measurement represents the difference in arrival time between two signals received from two different basestations, for example site 1 and site 2 or site 1 and site 3. The time differences may then be converted into constant distance differences between the UE and the two basestations, allowing the plotting of a hyperbola along which the UE must be located. The time difference between signals received from sites 1 and 3 thus results in Hyperbola 3-1, the time difference between signals received from sites 1 and 4 results in the Hyperbola 4-1 etc. In the illustrated case of four sites, three time differences may be measured, resulting in three hyperbolae along which the UE must be located. The intersection of the hyperbolae represents the location of the UE. It will be appreciated from the above description and from FIG. 1 that a minimum of three sites, generating two hyperbolae, is necessary to solve for the position coordinates of a single UE.
OTDOA offers advantages in that the required measurements may be performed with a UE in idle mode, and it offers a high degree of accuracy without requiring external technologies such as GPS. However, in asynchronous networks, the process is complicated by the timing difference in signals transmitted from the different basestations. The difference in observed arrival time in signals in an asynchronous network is not only a function of the geographic location of the UE with respect to the transmitting basestations, but is also dependent on the difference in the time at which the signals were transmitted from the basestations. This difference in transmission time between two basestations is known as the relative time difference (RTD) between the basestations. In order to generate the hyperbolae and solve for UE position, it is necessary to know the RTDs of the basestations involved in addition to their geographic location and the reported differences in arrival time. The drift effects discussed above mean that RTDs are not dynamically stable but vary with time, meaning they must be constantly updated. In order to address this, location measurement units (LMUs) are introduced into the network architecture to perform timing measurements for all local basestations and to store these measurements for later use in locating UEs within the network.
LMUs introduce additional cost and complexity to the network, and it would thus be desirable to be able to locate UEs without relying on the measured RTDs provided by LMUs. Various methods have been proposed to address this challenge, but all suffer from drawbacks of one kind or another, for example requiring information that may not always be available, or placing a heavy computational load on the locating entity through multiple rounds of iteration.
Another problem encountered in positioning algorithms in both synchronous and asynchronous networks is error generation as a result of local minima. If the global equation set, formed for example by the hyperbolae of FIG. 1, includes both a local and global minimum, a positioning algorithm is at risk of converging upon the local minimum, and so generating a positional error when locating a UE. This problem is encountered to a greater or lesser extent in the majority of positioning methods offering an alternative to GPS based techniques.